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      High-throughput field crop phenotyping: current status and challenges

      research-article
      1 , 2 , *
      Breeding Science
      Japanese Society of Breeding
      canopy architectural traits, field phenotyping, CNN, image sensors, SfM-MVS, LiDAR, UAS

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          Abstract

          In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            ImageNet Large Scale Visual Recognition Challenge

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              Distinctive Image Features from Scale-Invariant Keypoints

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                Author and article information

                Journal
                Breed Sci
                Breed Sci
                jsbbs
                Breeding Science
                Japanese Society of Breeding
                1344-7610
                1347-3735
                March 2022
                17 February 2022
                : 72
                : 1
                : 3-18
                Affiliations
                [1 ] Graduate School of Agriculture and Life Sciences, The University of Tokyo , Nishitokyo, Tokyo 188-0002, Japan
                [2 ] Plant Phenomics Research Center, Nanjing Agricultural University , Nanjing, China
                Author notes
                [* ]Corresponding author (e-mail: snino@ 123456g.ecc.u-tokyo.ac.jp )

                Communicated by Sachiko Isobe

                Article
                JST.JSTAGE/jsbbs/21069 21069
                10.1270/jsbbs.21069
                8987842
                36045897
                4c1e0625-6e1c-4663-b619-f7c301ea9770
                Copyright © 2022 by JAPANESE SOCIETY OF BREEDING

                This is an open-access article distributed under the terms of the Creative Commons Attribution (BY) License (CC-BY 4.0: https://creativecommons.org/licenses/by/4.0/).

                History
                : 31 August 2021
                : 16 December 2021
                Categories
                Invited Review

                Animal agriculture
                canopy architectural traits,field phenotyping,cnn,image sensors,sfm-mvs,lidar,uas
                Animal agriculture
                canopy architectural traits, field phenotyping, cnn, image sensors, sfm-mvs, lidar, uas

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